{"id":"W4401281194","doi":"10.1016/j.scitotenv.2024.175256","title":"Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Korea Environmental Industry and Technology Institute; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; Institute for Korea Spent Nuclear Fuel","keywords":"Hazardous waste; Hydrogeology; Calibration; Computer science; Probabilistic logic; Latin hypercube sampling; Uncertainty quantification; Fault detection and isolation; Reliability engineering; Environmental science; Data mining; Engineering; Monte Carlo method; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002451696,0.0009003518,0.0005256337,0.0004995879,0.000914679,0.00140126,0.002212765,0.002002255,0.00177211],"category_scores_gemma":[0.007344734,0.0003549097,0.0003303511,0.0004932092,0.0008207799,0.002371444,0.0007500991,0.001149783,0.0004438245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608912,"about_ca_system_score_gemma":0.002512437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02806911,"about_ca_topic_score_gemma":0.04434445,"domain_scores_codex":[0.9991092,0.0003723603,0.00003555261,0.0001449668,0.000214468,0.0001233823],"domain_scores_gemma":[0.9939749,0.002026791,0.0003641725,0.0007239203,0.00254166,0.0003686691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001273741,0.001975164,0.08364694,0.000350628,0.0001221235,0.000803812,0.001039923,0.4103525,0.06183889,0.004680524,0.01176717,0.4221486],"study_design_scores_gemma":[0.0002047806,0.001277442,0.01695169,0.00006156513,0.000138008,0.0003134411,0.001374843,0.9072632,0.0570929,0.006670762,0.00852972,0.0001216345],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6469793,0.0005609397,0.3251493,0.009971553,0.0002352521,0.0003815694,0.00031959,0.003487258,0.01291531],"genre_scores_gemma":[0.920366,0.0002062199,0.07670522,0.000264786,0.00003286915,0.00004851449,0.000104208,0.0001054271,0.002166685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02806911,"threshold_uncertainty_score":0.05581146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208214875247656,"score_gpt":0.2423931886302083,"score_spread":0.2203110398777318,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}